<p><it>Abstract</it>—In an effort to make object recognition efficient and accurate enough for real applications, we have developed three probabilistic techniques—sensor modeling, probabilistic hypothesis generation, and robust localization—which form the basis of a promising paradigm for object recognition. Our techniques effectively exploit prior knowledge to reduce the number of hypotheses that must be tested during recognition. Our recognition approach utilizes statistical constraints on the matches between image and model features. These statistical constraints are computed using a model of the entire sensing process—resulting in more realistic and tighter constraints on matches. The candidate hypotheses are pruned by probabilistic constraint satisfaction to select likely matches based on the image evidence and prior statistical constraints. The resulting hypotheses are ordered most-likely first for verification, thus minimizing unnecessary verifications. The reliability of the verification decision is significantly increased by the use of a robust localization algorithm. Our localization algorithm reliably locates objects despite partial occlusion and significant errors in initial location estimates. We have implemented these techniques in a system that recognizes polyhedral objects in range images. Our results demonstrate accurate recognition while greatly limiting the number of verifications.</p>